geoserver-rest VS Fiona

{% set name = 'geoserver-rest' %} {% set version = '0.1.0' %} package: name: {{ name|lower }} version: {{ version }} source: url: https://pypi.io/packages/source/{{ name[0] }}/{{ name }}/{{ name }}-{{ version }}.tar.gz sha256: 625327b23ee2ee77e839272b04d0b6e81b7dc98e0713e4e23d003a1bf2461551 build: noarch: python number: 0 script: "{{ PYTHON }} -m pip install . -vv" requirements: host: - python >=3.6 - pip run: - python >=3.6 - pip - gdal - seaborn - pycurl - psycopg2 test: imports: - geo commands: - pip check requires: - pip about: home: https://github.com/gicait/geoserver-rest license: MIT license_file: LICENSE summary: "The package for management of geospatial data in GeoServer." description: | The geoserver-rest package is useful for the management for geospatial data in GeoServer. The package is useful for the creating, updating and deleting geoserver workspaces, stores, layers, and style files. dev_url: https://github.com/gicait/geoserver-rest extra: recipe-maintainers: - iamtekson

Your issue is that you're using the default (old) binding to GDAL, based on Fiona [0].

You need to use pyogrio [1], its vectorized counterpart, instead. Make sure you use `engine="pyogrio"` when calling `to_file` [2]. Fiona does a loop in Python, while pyogrio is exclusively compiled. So pyogrio is usually about 10-15x faster than fiona. Soon, in pyogrio version 0.8, it will be another ~2-4x faster than pyogrio is now [3].

[0]: https://github.com/Toblerity/Fiona

[1]: https://github.com/geopandas/pyogrio

[2]: https://geopandas.org/en/stable/docs/reference/api/geopandas...

[3]: https://github.com/geopandas/pyogrio/pull/346

As has been said the definition of a GIS dev is far from written in stone, but to chime in from a personal standpoint: most of what I do is data wrangling/analysis with shapely/geopandas for vectors (or pygeos / fiona for performance when data volumes get large, but seeing as Shapely 2.0 just got released one can likely skip this part) + rasterio for rasters as well as parallelising these tasks for performance if needed (ray is great for that) and then performing machine learning learning against the data (mostly with sklearn and torch).

TypeError 10 refers to (https://github.com/Toblerity/Fiona/blob/master/fiona/_geometry.pyx):